Data-Driven Investments at JGSB

Background courses
- 665 Python for Business Analytics*
- 649 Data Mining for Business Analytics
- 679 Machine Learning for Business Analytics
Investments courses
- 638 Data-Driven Investments Equity*
- 639 Data-Driven Investments Credit*
- 767 Data-Driven Investments Lab
Data and procedures
- 100+ monthly features used by GKX
- financial ratios and growth rates
- momentum, volatility, beta, market cap, volume
- analyst forecasts and earnings surprises
- Industry membership
- Machine learning (random forests, boosted forests, neural networks) \(\rightarrow\) stock return predictions
= Optimized factor investing
Backtests
- Long best stocks and maybe short worst stocks
- Analyze portfolio returns
- Sharpe ratios and drawdowns
- CAPM alphas and information ratios
- Fama-French factor attribution analysis
- Group projects and presentations
- ML models for default prediction
- Full semester (spring)
- Prereq is either equity or credit
- More ML (cross validation, …)
- New data sources (insider trades, …)
- Implement models at Alpaca Brokerage using python API
- Weekly team reports on performance and model evaluations
- Several students taking the equity, credit, and lab courses are participating in the Chicago Quantitative Alliance investment competition.
- Run a long/short portfolio with no position > 5% and cash < 5% and no ETFs.
- Last year, a JGSB MBA team won the competition, finishing first in all three categories: return, compliance, and presentation.
- This year, at the 2/3 point